Hybrid Telemetry Fusion for Early Detection of Systemwide Failures

Abstract

Modern distributed systems generate vast and heterogeneous streams of operational data, including metrics, logs, events, traces, configuration snapshots, and network-level signals. Although each telemetry source provides valuable insights, they are typically analyzed in isolation, resulting in delayed understanding of emerging systemwide failures. As applications scale across clusters, nodes, services, and network domains, failures increasingly manifest as subtle cross-layer interactions rather than isolated component issues. Conventional approaches are therefore limited in their ability to detect failures early, correlate related signals, or capture the causal chain that leads to large-scale degradation. These limitations often result in reactive incident response, increased mean time to detection (MTTD), and an inability to predict systemwide impacts before end-users experience service disruption. This research proposes a Hybrid Telemetry Fusion framework designed to overcome these limitations by integrating diverse observability data into a unified, multi-dimensional representation of system health. Instead of treating telemetry streams independently, the proposed approach fuses metrics, logs, traces, and network signals to construct enriched cross-layer feature sets capable of revealing early indicators of cascading failures. The framework incorporates telemetry alignment, temporal correlation, semantic enrichment, and multi-source feature construction to enable a more holistic understanding of system behavior. The primary objective of this work is to address the current gap in early detection of large-scale failures by enabling the system to observe emerging anomalies that span multiple components, resource types, and operational layers. Specifically, the research aims to resolve the challenge of fragmented observability by creating a fusion-powered detection mechanism that identifies systemwide instability earlier than traditional monitoring techniques. By systematically integrating hybrid telemetry sources, the proposed framework seeks to detect fault propagation patterns, cross-component anomalies, and early warning signals that cannot be captured through single-source analysis. This approach directly targets the core limitation of existing observability systems—their inability to correlate multi-modal signals into a coherent, early indicator of impending systemwide failure.